{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chap6 Statistical Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A small Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:29.745191Z",
     "start_time": "2019-04-29T01:44:29.599Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t201 obs. of  3 variables:\n",
      " $ outcome          : Ord.factor w/ 2 levels \"paid off\"<\"default\": NA 2 1 1 2 1 2 1 2 2 ...\n",
      " $ payment_inc_ratio: num  9 5.47 6.9 11.15 3.72 ...\n",
      " $ dti              : num  22.5 21.33 8.97 1.83 10.81 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>outcome</th><th scope=col>payment_inc_ratio</th><th scope=col>dti</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>NA      </td><td>9.00000 </td><td>22.50   </td></tr>\n",
       "\t<tr><td>default </td><td>5.46933 </td><td>21.33   </td></tr>\n",
       "\t<tr><td>paid off</td><td>6.90294 </td><td> 8.97   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " outcome & payment\\_inc\\_ratio & dti\\\\\n",
       "\\hline\n",
       "\t NA       & 9.00000  & 22.50   \\\\\n",
       "\t default  & 5.46933  & 21.33   \\\\\n",
       "\t paid off & 6.90294  &  8.97   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| outcome | payment_inc_ratio | dti |\n",
       "|---|---|---|\n",
       "| NA       | 9.00000  | 22.50    |\n",
       "| default  | 5.46933  | 21.33    |\n",
       "| paid off | 6.90294  |  8.97    |\n",
       "\n"
      ],
      "text/plain": [
       "  outcome  payment_inc_ratio dti  \n",
       "1 NA       9.00000           22.50\n",
       "2 default  5.46933           21.33\n",
       "3 paid off 6.90294            8.97"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan200 = read.csv('../psds_data/loan200.csv')\n",
    "loan200$outcome <- ordered(loan200$outcome, levels=c('paid off', 'default'))\n",
    "str(loan200)\n",
    "head(loan200, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:30.161860Z",
     "start_time": "2019-04-29T01:44:29.613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(FNN)\n",
    "## the first row of loan200 is the target data\n",
    "newloan <- loan200[1, 2:3, drop=FALSE]\n",
    "knn_pred <- knn(train=loan200[-1, 2:3], test=newloan, cl=loan200[-1, 1])\n",
    "knn_pred == 'default'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意， 这里作者书上给的代码有问题，源码文件是对的，这里按照源码文件写。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T02:09:06.952994Z",
     "start_time": "2019-04-28T02:09:06.906Z"
    }
   },
   "source": [
    "##### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:30.674171Z",
     "start_time": "2019-04-29T01:44:29.625Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t45342 obs. of  21 variables:\n",
      " $ X                : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ status           : Factor w/ 3 levels \"Charged Off\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ loan_amnt        : int  2500 5600 5375 9000 10000 21000 6000 15000 5000 5000 ...\n",
      " $ term             : Factor w/ 2 levels \"36 months\",\"60 months\": 2 2 2 1 1 1 1 1 2 1 ...\n",
      " $ annual_inc       : int  30000 40000 15000 30000 100000 105000 76000 60000 50004 100000 ...\n",
      " $ dti              : num  1 5.55 18.08 10.08 7.06 ...\n",
      " $ payment_inc_ratio: num  2.39 4.57 9.72 12.22 3.91 ...\n",
      " $ revol_bal        : int  1687 5210 9279 10452 11997 32135 5963 5872 4345 74351 ...\n",
      " $ revol_util       : num  9.4 32.6 36.5 91.7 55.5 90.3 29.7 57.6 59.5 62.1 ...\n",
      " $ purpose          : Factor w/ 12 levels \"car\",\"credit_card\",..: 1 10 9 3 9 3 6 3 9 3 ...\n",
      " $ home_ownership   : Factor w/ 4 levels \"MORTGAGE\",\"OTHER\",..: 4 3 4 4 4 4 4 4 4 1 ...\n",
      " $ delinq_2yrs_zero : int  1 1 1 1 1 1 1 1 0 1 ...\n",
      " $ pub_rec_zero     : int  1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ open_acc         : int  3 11 2 4 14 7 7 7 14 17 ...\n",
      " $ grade            : num  4.8 1.4 6 4.2 5.4 5.8 5.6 4.4 3.4 7 ...\n",
      " $ outcome          : Factor w/ 2 levels \"default\",\"paid off\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ emp_length       : int  1 5 1 1 4 11 2 10 3 11 ...\n",
      " $ purpose_         : Factor w/ 7 levels \"credit_card\",..: 4 7 6 2 6 2 4 2 6 2 ...\n",
      " $ home_            : Factor w/ 3 levels \"MORTGAGE\",\"OWN\",..: 3 2 3 3 3 3 3 3 3 1 ...\n",
      " $ emp_len_         : Factor w/ 2 levels \" < 1 Year\",\" > 1 Year\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ borrower_score   : num  0.65 0.8 0.6 0.5 0.55 0.4 0.7 0.5 0.45 0.5 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>status</th><th scope=col>loan_amnt</th><th scope=col>term</th><th scope=col>annual_inc</th><th scope=col>dti</th><th scope=col>payment_inc_ratio</th><th scope=col>revol_bal</th><th scope=col>revol_util</th><th scope=col>purpose</th><th scope=col>⋯</th><th scope=col>delinq_2yrs_zero</th><th scope=col>pub_rec_zero</th><th scope=col>open_acc</th><th scope=col>grade</th><th scope=col>outcome</th><th scope=col>emp_length</th><th scope=col>purpose_</th><th scope=col>home_</th><th scope=col>emp_len_</th><th scope=col>borrower_score</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                                     </td><td>Charged Off                                           </td><td>2500                                                  </td><td>60 months                                             </td><td>30000                                                 </td><td> 1.00                                                 </td><td>2.3932                                                </td><td>1687                                                  </td><td> 9.4                                                  </td><td><span style=white-space:pre-wrap>car           </span></td><td>⋯                                                     </td><td>1                                                     </td><td>1                                                     </td><td> 3                                                    </td><td>4.8                                                   </td><td>default                                               </td><td>1                                                     </td><td>major_purchase                                        </td><td>RENT                                                  </td><td> &gt; 1 Year                                          </td><td>0.65                                                  </td></tr>\n",
       "\t<tr><td>2             </td><td>Charged Off   </td><td>5600          </td><td>60 months     </td><td>40000         </td><td> 5.55         </td><td>4.5717        </td><td>5210          </td><td>32.6          </td><td>small_business</td><td>⋯             </td><td>1             </td><td>1             </td><td>11            </td><td>1.4           </td><td>default       </td><td>5             </td><td>small_business</td><td>OWN           </td><td> &gt; 1 Year  </td><td>0.80          </td></tr>\n",
       "\t<tr><td>3                                                     </td><td>Charged Off                                           </td><td>5375                                                  </td><td>60 months                                             </td><td>15000                                                 </td><td>18.08                                                 </td><td>9.7160                                                </td><td>9279                                                  </td><td>36.5                                                  </td><td><span style=white-space:pre-wrap>other         </span></td><td>⋯                                                     </td><td>1                                                     </td><td>1                                                     </td><td> 2                                                    </td><td>6.0                                                   </td><td>default                                               </td><td>1                                                     </td><td><span style=white-space:pre-wrap>other         </span></td><td>RENT                                                  </td><td> &gt; 1 Year                                          </td><td>0.60                                                  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       " X & status & loan\\_amnt & term & annual\\_inc & dti & payment\\_inc\\_ratio & revol\\_bal & revol\\_util & purpose & ⋯ & delinq\\_2yrs\\_zero & pub\\_rec\\_zero & open\\_acc & grade & outcome & emp\\_length & purpose\\_ & home\\_ & emp\\_len\\_ & borrower\\_score\\\\\n",
       "\\hline\n",
       "\t 1                & Charged Off      & 2500             & 60 months        & 30000            &  1.00            & 2.3932           & 1687             &  9.4             & car              & ⋯                & 1                & 1                &  3               & 4.8              & default          & 1                & major\\_purchase & RENT             &  > 1 Year        & 0.65            \\\\\n",
       "\t 2                & Charged Off      & 5600             & 60 months        & 40000            &  5.55            & 4.5717           & 5210             & 32.6             & small\\_business & ⋯                & 1                & 1                & 11               & 1.4              & default          & 5                & small\\_business & OWN              &  > 1 Year        & 0.80            \\\\\n",
       "\t 3              & Charged Off    & 5375           & 60 months      & 15000          & 18.08          & 9.7160         & 9279           & 36.5           & other          & ⋯              & 1              & 1              &  2             & 6.0            & default        & 1              & other          & RENT           &  > 1 Year      & 0.60          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X | status | loan_amnt | term | annual_inc | dti | payment_inc_ratio | revol_bal | revol_util | purpose | ⋯ | delinq_2yrs_zero | pub_rec_zero | open_acc | grade | outcome | emp_length | purpose_ | home_ | emp_len_ | borrower_score |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1              | Charged Off    | 2500           | 60 months      | 30000          |  1.00          | 2.3932         | 1687           |  9.4           | car            | ⋯              | 1              | 1              |  3             | 4.8            | default        | 1              | major_purchase | RENT           |  > 1 Year      | 0.65           |\n",
       "| 2              | Charged Off    | 5600           | 60 months      | 40000          |  5.55          | 4.5717         | 5210           | 32.6           | small_business | ⋯              | 1              | 1              | 11             | 1.4            | default        | 5              | small_business | OWN            |  > 1 Year      | 0.80           |\n",
       "| 3              | Charged Off    | 5375           | 60 months      | 15000          | 18.08          | 9.7160         | 9279           | 36.5           | other          | ⋯              | 1              | 1              |  2             | 6.0            | default        | 1              | other          | RENT           |  > 1 Year      | 0.60           |\n",
       "\n"
      ],
      "text/plain": [
       "  X status      loan_amnt term      annual_inc dti   payment_inc_ratio\n",
       "1 1 Charged Off 2500      60 months 30000       1.00 2.3932           \n",
       "2 2 Charged Off 5600      60 months 40000       5.55 4.5717           \n",
       "3 3 Charged Off 5375      60 months 15000      18.08 9.7160           \n",
       "  revol_bal revol_util purpose        ⋯ delinq_2yrs_zero pub_rec_zero open_acc\n",
       "1 1687       9.4       car            ⋯ 1                1             3      \n",
       "2 5210      32.6       small_business ⋯ 1                1            11      \n",
       "3 9279      36.5       other          ⋯ 1                1             2      \n",
       "  grade outcome emp_length purpose_       home_ emp_len_  borrower_score\n",
       "1 4.8   default 1          major_purchase RENT   > 1 Year 0.65          \n",
       "2 1.4   default 5          small_business OWN    > 1 Year 0.80          \n",
       "3 6.0   default 1          other          RENT   > 1 Year 0.60          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_data = read.csv('../psds_data/loan_data.csv')\n",
    "str(loan_data)\n",
    "head(loan_data, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.368746Z",
     "start_time": "2019-04-29T01:44:29.632Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t45342 obs. of  19 variables:\n",
      " $ loan_amnt        : int  2500 5600 5375 9000 10000 21000 6000 15000 5000 5000 ...\n",
      " $ term             : Factor w/ 2 levels \"36 months\",\"60 months\": 2 2 2 1 1 1 1 1 2 1 ...\n",
      " $ annual_inc       : int  30000 40000 15000 30000 100000 105000 76000 60000 50004 100000 ...\n",
      " $ dti              : num  1 5.55 18.08 10.08 7.06 ...\n",
      " $ payment_inc_ratio: num  2.39 4.57 9.72 12.22 3.91 ...\n",
      " $ revol_bal        : int  1687 5210 9279 10452 11997 32135 5963 5872 4345 74351 ...\n",
      " $ revol_util       : num  9.4 32.6 36.5 91.7 55.5 90.3 29.7 57.6 59.5 62.1 ...\n",
      " $ purpose          : Factor w/ 12 levels \"car\",\"credit_card\",..: 1 10 9 3 9 3 6 3 9 3 ...\n",
      " $ home_ownership   : Factor w/ 4 levels \"MORTGAGE\",\"OTHER\",..: 4 3 4 4 4 4 4 4 4 1 ...\n",
      " $ delinq_2yrs_zero : int  1 1 1 1 1 1 1 1 0 1 ...\n",
      " $ pub_rec_zero     : int  1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ open_acc         : int  3 11 2 4 14 7 7 7 14 17 ...\n",
      " $ grade            : num  4.8 1.4 6 4.2 5.4 5.8 5.6 4.4 3.4 7 ...\n",
      " $ outcome          : Factor w/ 2 levels \"default\",\"paid off\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ emp_length       : int  1 5 1 1 4 11 2 10 3 11 ...\n",
      " $ purpose_         : Factor w/ 7 levels \"credit_card\",..: 4 7 6 2 6 2 4 2 6 2 ...\n",
      " $ home_            : Factor w/ 3 levels \"MORTGAGE\",\"OWN\",..: 3 2 3 3 3 3 3 3 3 1 ...\n",
      " $ emp_len_         : Factor w/ 2 levels \" < 1 Year\",\" > 1 Year\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ borrower_score   : num  0.65 0.8 0.6 0.5 0.55 0.4 0.7 0.5 0.45 0.5 ...\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)\n",
    "# 去除X与status两列\n",
    "loan_data <- select(loan_data, -X, -status)\n",
    "str(loan_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.460521Z",
     "start_time": "2019-04-29T01:44:29.641Z"
    }
   },
   "outputs": [],
   "source": [
    "loan_df <- model.matrix(~ -1 + payment_inc_ratio + dti + revol_bal + revol_util, data=loan_data)\n",
    "newloan = loan_df[1,, drop=FALSE]\n",
    "loan_df = loan_df[-1,]\n",
    "outcome <- loan_data[-1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`model.matrix`:model.matrix creates a design (or model) matrix, e.g., by expanding factors to a set of dummy variables (depending on the contrasts) and expanding interactions similarly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.486234Z",
     "start_time": "2019-04-29T01:44:29.650Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " int [1:45341] 5600 5375 9000 10000 21000 6000 15000 5000 5000 15000 ...\n"
     ]
    }
   ],
   "source": [
    "#str(loan_df)\n",
    "str(outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.571631Z",
     "start_time": "2019-04-29T01:44:29.654Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "4000\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t'4000'\n",
       "</details>"
      ],
      "text/latex": [
       "4000\n",
       "\\emph{Levels}: '4000'"
      ],
      "text/markdown": [
       "4000\n",
       "**Levels**: '4000'"
      ],
      "text/plain": [
       "[1] 4000\n",
       "attr(,\"nn.index\")\n",
       "      [,1]  [,2]  [,3]  [,4]  [,5]\n",
       "[1,] 35536 33651 25863 42953 43599\n",
       "attr(,\"nn.dist\")\n",
       "         [,1]     [,2]     [,3]     [,4]     [,5]\n",
       "[1,] 1.555631 5.640407 7.138838 8.842243 8.972774\n",
       "Levels: 4000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_pred <- knn(train=loan_df, test=newloan, cl=outcome, k=5)\n",
    "knn_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.606856Z",
     "start_time": "2019-04-29T01:44:29.659Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>payment_inc_ratio</th><th scope=col>dti</th><th scope=col>revol_bal</th><th scope=col>revol_util</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>35537</th><td>1.47212</td><td>1.46   </td><td>1686   </td><td>10.0   </td></tr>\n",
       "\t<tr><th scope=row>33652</th><td>3.38178</td><td>6.37   </td><td>1688   </td><td> 8.4   </td></tr>\n",
       "\t<tr><th scope=row>25864</th><td>2.36303</td><td>1.39   </td><td>1691   </td><td> 3.5   </td></tr>\n",
       "\t<tr><th scope=row>42954</th><td>1.28160</td><td>7.14   </td><td>1684   </td><td> 3.9   </td></tr>\n",
       "\t<tr><th scope=row>43600</th><td>4.12244</td><td>8.98   </td><td>1684   </td><td> 7.2   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & payment\\_inc\\_ratio & dti & revol\\_bal & revol\\_util\\\\\n",
       "\\hline\n",
       "\t35537 & 1.47212 & 1.46    & 1686    & 10.0   \\\\\n",
       "\t33652 & 3.38178 & 6.37    & 1688    &  8.4   \\\\\n",
       "\t25864 & 2.36303 & 1.39    & 1691    &  3.5   \\\\\n",
       "\t42954 & 1.28160 & 7.14    & 1684    &  3.9   \\\\\n",
       "\t43600 & 4.12244 & 8.98    & 1684    &  7.2   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | payment_inc_ratio | dti | revol_bal | revol_util |\n",
       "|---|---|---|---|---|\n",
       "| 35537 | 1.47212 | 1.46    | 1686    | 10.0    |\n",
       "| 33652 | 3.38178 | 6.37    | 1688    |  8.4    |\n",
       "| 25864 | 2.36303 | 1.39    | 1691    |  3.5    |\n",
       "| 42954 | 1.28160 | 7.14    | 1684    |  3.9    |\n",
       "| 43600 | 4.12244 | 8.98    | 1684    |  7.2    |\n",
       "\n"
      ],
      "text/plain": [
       "      payment_inc_ratio dti  revol_bal revol_util\n",
       "35537 1.47212           1.46 1686      10.0      \n",
       "33652 3.38178           6.37 1688       8.4      \n",
       "25864 2.36303           1.39 1691       3.5      \n",
       "42954 1.28160           7.14 1684       3.9      \n",
       "43600 4.12244           8.98 1684       7.2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_df[attr(knn_pred,\"nn.index\"),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:32.711211Z",
     "start_time": "2019-04-29T01:44:29.668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>payment_inc_ratio</th><th scope=col>dti</th><th scope=col>revol_bal</th><th scope=col>revol_util</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>24088</th><td> 2.64252</td><td>18.25   </td><td>15067   </td><td>59.6    </td></tr>\n",
       "\t<tr><th scope=row>41565</th><td> 7.25964</td><td>15.17   </td><td>19727   </td><td>29.3    </td></tr>\n",
       "\t<tr><th scope=row>31233</th><td> 2.59435</td><td> 4.73   </td><td>19101   </td><td>79.6    </td></tr>\n",
       "\t<tr><th scope=row>43361</th><td>10.19650</td><td>18.96   </td><td> 9913   </td><td>75.1    </td></tr>\n",
       "\t<tr><th scope=row>19264</th><td> 9.43726</td><td>21.05   </td><td>14658   </td><td>80.1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & payment\\_inc\\_ratio & dti & revol\\_bal & revol\\_util\\\\\n",
       "\\hline\n",
       "\t24088 &  2.64252 & 18.25    & 15067    & 59.6    \\\\\n",
       "\t41565 &  7.25964 & 15.17    & 19727    & 29.3    \\\\\n",
       "\t31233 &  2.59435 &  4.73    & 19101    & 79.6    \\\\\n",
       "\t43361 & 10.19650 & 18.96    &  9913    & 75.1    \\\\\n",
       "\t19264 &  9.43726 & 21.05    & 14658    & 80.1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | payment_inc_ratio | dti | revol_bal | revol_util |\n",
       "|---|---|---|---|---|\n",
       "| 24088 |  2.64252 | 18.25    | 15067    | 59.6     |\n",
       "| 41565 |  7.25964 | 15.17    | 19727    | 29.3     |\n",
       "| 31233 |  2.59435 |  4.73    | 19101    | 79.6     |\n",
       "| 43361 | 10.19650 | 18.96    |  9913    | 75.1     |\n",
       "| 19264 |  9.43726 | 21.05    | 14658    | 80.1     |\n",
       "\n"
      ],
      "text/plain": [
       "      payment_inc_ratio dti   revol_bal revol_util\n",
       "24088  2.64252          18.25 15067     59.6      \n",
       "41565  7.25964          15.17 19727     29.3      \n",
       "31233  2.59435           4.73 19101     79.6      \n",
       "43361 10.19650          18.96  9913     75.1      \n",
       "19264  9.43726          21.05 14658     80.1      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_std <- scale(loan_df)\n",
    "target_std = loan_std[1,, drop=FALSE]\n",
    "loan_std = loan_std[-1,]\n",
    "# 此处outcome应该减去前两列\n",
    "outcome <- loan_data[-c(1,2),1]\n",
    "knn_pred <- knn(train=loan_std, test=target_std, cl=outcome, k=5)\n",
    "loan_df[attr(knn_pred,\"nn.index\"), ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T02:25:05.981428Z",
     "start_time": "2019-04-28T02:25:05.932Z"
    }
   },
   "source": [
    "这里书上写的和作者给的源码全是错的Orz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### KNN as a Feature Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:38.802132Z",
     "start_time": "2019-04-29T01:44:29.675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.050   0.400   0.500   0.499   0.600   1.000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "borrow_df <- model.matrix(~ -1 + dti + revol_bal + revol_util + open_acc +\n",
    "                            delinq_2yrs_zero + pub_rec_zero, data=loan_data)\n",
    "borrow_knn <- knn(borrow_df, test=borrow_df, cl=loan_data[, 'outcome'], prob=TRUE, k=20)\n",
    "prob <- attr(borrow_knn, \"prob\")\n",
    "borrow_feature <- ifelse(borrow_knn=='default', 1-prob, prob)\n",
    "summary(borrow_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:38.822738Z",
     "start_time": "2019-04-29T01:44:29.679Z"
    }
   },
   "outputs": [],
   "source": [
    "loan_data$borrower_score <- borrow_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tree Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:39.305064Z",
     "start_time": "2019-04-29T01:44:29.682Z"
    }
   },
   "outputs": [],
   "source": [
    "library(rpart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:40.357163Z",
     "start_time": "2019-04-29T01:44:29.686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAMAAABvmDbGAAAC8VBMVEUAAAABAQECAgIDAwME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\n9t1XXhPKAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_tree <- rpart(outcome ~ borrower_score + payment_inc_ratio,\n",
    "                  data=loan_data, control=rpart.control(cp=0.005))\n",
    "options(repr.plot.width=7, repr.plot.height=5)\n",
    "plot(loan_tree)\n",
    "text(loan_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:40.389851Z",
     "start_time": "2019-04-29T01:44:29.692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n= 45342 \n",
       "\n",
       "node), split, n, loss, yval, (yprob)\n",
       "      * denotes terminal node\n",
       "\n",
       " 1) root 45342 22671 default (0.5000000 0.5000000)  \n",
       "   2) borrower_score< 0.475 19422  7143 default (0.6322212 0.3677788) *\n",
       "   3) borrower_score>=0.475 25920 10392 paid off (0.4009259 0.5990741)  \n",
       "     6) payment_inc_ratio>=7.762325 11528  5619 paid off (0.4874219 0.5125781)  \n",
       "      12) borrower_score< 0.625 8549  4062 default (0.5248567 0.4751433)  \n",
       "        24) payment_inc_ratio>=11.31995 4009  1690 default (0.5784485 0.4215515) *\n",
       "        25) payment_inc_ratio< 11.31995 4540  2168 paid off (0.4775330 0.5224670) *\n",
       "      13) borrower_score>=0.625 2979  1132 paid off (0.3799933 0.6200067) *\n",
       "     7) payment_inc_ratio< 7.762325 14392  4773 paid off (0.3316426 0.6683574) *"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bagging and the Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:40.838085Z",
     "start_time": "2019-04-29T01:44:29.697Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "randomForest 4.6-14\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(randomForest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:41.793049Z",
     "start_time": "2019-04-29T01:44:29.702Z"
    }
   },
   "outputs": [],
   "source": [
    "loan3000 <- read.csv('../psds_data/loan3000.csv')\n",
    "rf <- randomForest(outcome ~ borrower_score + payment_inc_ratio, data = loan3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:41.820669Z",
     "start_time": "2019-04-29T01:44:29.707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       " randomForest(formula = outcome ~ borrower_score + payment_inc_ratio,      data = loan3000) \n",
       "               Type of random forest: classification\n",
       "                     Number of trees: 500\n",
       "No. of variables tried at each split: 1\n",
       "\n",
       "        OOB estimate of  error rate: 39.07%\n",
       "Confusion matrix:\n",
       "         default paid off class.error\n",
       "default      885      560   0.3875433\n",
       "paid off     612      943   0.3935691"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:44.977334Z",
     "start_time": "2019-04-29T01:44:29.711Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘ggplot2’\n",
      "\n",
      "The following object is masked from ‘package:randomForest’:\n",
      "\n",
      "    margin\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAMAAABvmDbGAAADAFBMVEUAAAABAQECAgIDAwME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\nCHEAikSIA1AkQhzgX/ES0gjZfLH7AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_df <- data.frame(error_rate=rf$err.rate[, 'OOB'],\n",
    "                      num_trees = 1:rf$ntree)\n",
    "library(ggplot2)\n",
    "ggplot(data=error_df, aes(x=num_trees, y=error_rate)) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:44:45.466755Z",
     "start_time": "2019-04-29T01:44:29.717Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 18 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAIAAADXJFGjAAAACXBIWXMAABJ0AAASdAHeZh94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\nJL1PIc6YaEYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred <- predict(rf, prob=TRUE)  # 书上代码错的\n",
    "rf_df <- cbind(loan3000, pred=pred)\n",
    "ggplot(data=rf_df, aes(x=borrower_score, y=payment_inc_ratio, color=pred,\n",
    "                      shape=pred)) +\n",
    "        geom_point(alpha=0.6, size=2)+\n",
    "        scale_shape_manual(values = c(46, 4))+\n",
    "        scale_x_continuous(expand=c(0,0)) + \n",
    "        scale_y_continuous(expand=c(0,0), lim=c(0, 20)) + \n",
    "        theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:48:19.874612Z",
     "start_time": "2019-04-29T01:44:29.724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       " randomForest(formula = outcome ~ ., data = loan_data, importance = TRUE) \n",
       "               Type of random forest: classification\n",
       "                     Number of trees: 500\n",
       "No. of variables tried at each split: 4\n",
       "\n",
       "        OOB estimate of  error rate: 33.91%\n",
       "Confusion matrix:\n",
       "         default paid off class.error\n",
       "default    15116     7555   0.3332451\n",
       "paid off    7819    14852   0.3448899"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_all <- randomForest(outcome ~ ., data=loan_data, importance = TRUE)\n",
    "rf_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:48:20.203322Z",
     "start_time": "2019-04-29T01:44:29.730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAMAAABvmDbGAAADAFBMVEUAAAABAQECAgIDAwME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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “rf_all”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImpPlot(rf_all, type=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:48:20.286388Z",
     "start_time": "2019-04-29T01:44:29.735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAMAAABvmDbGAAADAFBMVEUAAAABAQECAgIDAwME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\nAoBIAAgAIgEgAIgEgAAgEgAC+P+ZGH8FD2iQSgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “rf_all”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImpPlot(rf_all, type=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:50:55.286187Z",
     "start_time": "2019-04-29T01:50:55.250Z"
    }
   },
   "source": [
    "##### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T01:55:05.154928Z",
     "start_time": "2019-04-29T01:55:01.306Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘xgboost’\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    slice\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(xgboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:00:22.827512Z",
     "start_time": "2019-04-29T02:00:22.552Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-error:0.365667 \n",
      "[2]\ttrain-error:0.349667 \n",
      "[3]\ttrain-error:0.348000 \n",
      "[4]\ttrain-error:0.338333 \n",
      "[5]\ttrain-error:0.339667 \n",
      "[6]\ttrain-error:0.335333 \n",
      "[7]\ttrain-error:0.332667 \n",
      "[8]\ttrain-error:0.330333 \n",
      "[9]\ttrain-error:0.327333 \n",
      "[10]\ttrain-error:0.327667 \n",
      "[11]\ttrain-error:0.329000 \n",
      "[12]\ttrain-error:0.321667 \n",
      "[13]\ttrain-error:0.317333 \n",
      "[14]\ttrain-error:0.319333 \n",
      "[15]\ttrain-error:0.322000 \n",
      "[16]\ttrain-error:0.315667 \n",
      "[17]\ttrain-error:0.315667 \n",
      "[18]\ttrain-error:0.312667 \n",
      "[19]\ttrain-error:0.307000 \n",
      "[20]\ttrain-error:0.309000 \n",
      "[21]\ttrain-error:0.308000 \n",
      "[22]\ttrain-error:0.308667 \n",
      "[23]\ttrain-error:0.307667 \n",
      "[24]\ttrain-error:0.303333 \n",
      "[25]\ttrain-error:0.301667 \n",
      "[26]\ttrain-error:0.303667 \n",
      "[27]\ttrain-error:0.301333 \n",
      "[28]\ttrain-error:0.302000 \n",
      "[29]\ttrain-error:0.302000 \n",
      "[30]\ttrain-error:0.299333 \n",
      "[31]\ttrain-error:0.297333 \n",
      "[32]\ttrain-error:0.298000 \n",
      "[33]\ttrain-error:0.296333 \n",
      "[34]\ttrain-error:0.294667 \n",
      "[35]\ttrain-error:0.294000 \n",
      "[36]\ttrain-error:0.294000 \n",
      "[37]\ttrain-error:0.294667 \n",
      "[38]\ttrain-error:0.293667 \n",
      "[39]\ttrain-error:0.293333 \n",
      "[40]\ttrain-error:0.292333 \n",
      "[41]\ttrain-error:0.289000 \n",
      "[42]\ttrain-error:0.287667 \n",
      "[43]\ttrain-error:0.286667 \n",
      "[44]\ttrain-error:0.284000 \n",
      "[45]\ttrain-error:0.283000 \n",
      "[46]\ttrain-error:0.284000 \n",
      "[47]\ttrain-error:0.283667 \n",
      "[48]\ttrain-error:0.280667 \n",
      "[49]\ttrain-error:0.279667 \n",
      "[50]\ttrain-error:0.278333 \n",
      "[51]\ttrain-error:0.278333 \n",
      "[52]\ttrain-error:0.277667 \n",
      "[53]\ttrain-error:0.279000 \n",
      "[54]\ttrain-error:0.276333 \n",
      "[55]\ttrain-error:0.270667 \n",
      "[56]\ttrain-error:0.268667 \n",
      "[57]\ttrain-error:0.262333 \n",
      "[58]\ttrain-error:0.264667 \n",
      "[59]\ttrain-error:0.266000 \n",
      "[60]\ttrain-error:0.268000 \n",
      "[61]\ttrain-error:0.266000 \n",
      "[62]\ttrain-error:0.266333 \n",
      "[63]\ttrain-error:0.266667 \n",
      "[64]\ttrain-error:0.265667 \n",
      "[65]\ttrain-error:0.266333 \n",
      "[66]\ttrain-error:0.259000 \n",
      "[67]\ttrain-error:0.260000 \n",
      "[68]\ttrain-error:0.259667 \n",
      "[69]\ttrain-error:0.256667 \n",
      "[70]\ttrain-error:0.259667 \n",
      "[71]\ttrain-error:0.258667 \n",
      "[72]\ttrain-error:0.255333 \n",
      "[73]\ttrain-error:0.255333 \n",
      "[74]\ttrain-error:0.254333 \n",
      "[75]\ttrain-error:0.254000 \n",
      "[76]\ttrain-error:0.256000 \n",
      "[77]\ttrain-error:0.254333 \n",
      "[78]\ttrain-error:0.252667 \n",
      "[79]\ttrain-error:0.254333 \n",
      "[80]\ttrain-error:0.252333 \n",
      "[81]\ttrain-error:0.248333 \n",
      "[82]\ttrain-error:0.249667 \n",
      "[83]\ttrain-error:0.248333 \n",
      "[84]\ttrain-error:0.248000 \n",
      "[85]\ttrain-error:0.247333 \n",
      "[86]\ttrain-error:0.248000 \n",
      "[87]\ttrain-error:0.249000 \n",
      "[88]\ttrain-error:0.245000 \n",
      "[89]\ttrain-error:0.246667 \n",
      "[90]\ttrain-error:0.244667 \n",
      "[91]\ttrain-error:0.245000 \n",
      "[92]\ttrain-error:0.243333 \n",
      "[93]\ttrain-error:0.243333 \n",
      "[94]\ttrain-error:0.244333 \n",
      "[95]\ttrain-error:0.244333 \n",
      "[96]\ttrain-error:0.242333 \n",
      "[97]\ttrain-error:0.242667 \n",
      "[98]\ttrain-error:0.243333 \n",
      "[99]\ttrain-error:0.244000 \n",
      "[100]\ttrain-error:0.243000 \n"
     ]
    }
   ],
   "source": [
    "predictors <- data.matrix(loan3000[, c('borrower_score', 'payment_inc_ratio')])\n",
    "label <- as.numeric(loan3000[, 'outcome']) - 1\n",
    "xgb <- xgboost(data=predictors, label=label,\n",
    "              objective=\"binary:logistic\",\n",
    "              params=list(subsample=.63, eta=0.1), nrounds=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:00:34.733683Z",
     "start_time": "2019-04-29T02:00:34.339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAIAAADXJFGjAAAACXBIWXMAABJ0AAASdAHeZh94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\n/wElPGjLUPuS/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred <- predict(xgb, newdata=predictors)\n",
    "xgb_df <- cbind(loan3000, pred_default=pred>0.5, prob_default=pred)\n",
    "ggplot(data=xgb_df, aes(x=borrower_score, y=payment_inc_ratio,\n",
    "                       color=pred_default, shape=pred_default)) +\n",
    "        geom_point(alpha=0.6, size=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Regularization: Avoiding Overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:07:58.877357Z",
     "start_time": "2019-04-29T02:07:49.038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-error:0.338011 \n",
      "[2]\ttrain-error:0.329551 \n",
      "[3]\ttrain-error:0.324260 \n",
      "[4]\ttrain-error:0.323043 \n",
      "[5]\ttrain-error:0.319620 \n",
      "[6]\ttrain-error:0.317696 \n",
      "[7]\ttrain-error:0.315206 \n",
      "[8]\ttrain-error:0.314159 \n",
      "[9]\ttrain-error:0.312376 \n",
      "[10]\ttrain-error:0.309547 \n",
      "[11]\ttrain-error:0.308358 \n",
      "[12]\ttrain-error:0.305953 \n",
      "[13]\ttrain-error:0.305133 \n",
      "[14]\ttrain-error:0.301935 \n",
      "[15]\ttrain-error:0.300719 \n",
      "[16]\ttrain-error:0.299162 \n",
      "[17]\ttrain-error:0.297635 \n",
      "[18]\ttrain-error:0.295993 \n",
      "[19]\ttrain-error:0.295173 \n",
      "[20]\ttrain-error:0.292372 \n",
      "[21]\ttrain-error:0.291183 \n",
      "[22]\ttrain-error:0.290476 \n",
      "[23]\ttrain-error:0.289033 \n",
      "[24]\ttrain-error:0.288807 \n",
      "[25]\ttrain-error:0.287052 \n",
      "[26]\ttrain-error:0.286203 \n",
      "[27]\ttrain-error:0.285524 \n",
      "[28]\ttrain-error:0.284732 \n",
      "[29]\ttrain-error:0.283685 \n",
      "[30]\ttrain-error:0.281789 \n",
      "[31]\ttrain-error:0.280771 \n",
      "[32]\ttrain-error:0.280658 \n",
      "[33]\ttrain-error:0.278734 \n",
      "[34]\ttrain-error:0.277262 \n",
      "[35]\ttrain-error:0.276385 \n",
      "[36]\ttrain-error:0.274970 \n",
      "[37]\ttrain-error:0.274716 \n",
      "[38]\ttrain-error:0.274121 \n",
      "[39]\ttrain-error:0.273273 \n",
      "[40]\ttrain-error:0.272282 \n",
      "[41]\ttrain-error:0.272282 \n",
      "[42]\ttrain-error:0.270783 \n",
      "[43]\ttrain-error:0.269792 \n",
      "[44]\ttrain-error:0.268830 \n",
      "[45]\ttrain-error:0.267246 \n",
      "[46]\ttrain-error:0.265944 \n",
      "[47]\ttrain-error:0.265237 \n",
      "[48]\ttrain-error:0.264162 \n",
      "[49]\ttrain-error:0.262690 \n",
      "[50]\ttrain-error:0.260653 \n",
      "[51]\ttrain-error:0.259861 \n",
      "[52]\ttrain-error:0.259550 \n",
      "[53]\ttrain-error:0.259210 \n",
      "[54]\ttrain-error:0.258474 \n",
      "[55]\ttrain-error:0.257512 \n",
      "[56]\ttrain-error:0.255192 \n",
      "[57]\ttrain-error:0.253919 \n",
      "[58]\ttrain-error:0.253240 \n",
      "[59]\ttrain-error:0.252674 \n",
      "[60]\ttrain-error:0.251712 \n",
      "[61]\ttrain-error:0.251429 \n",
      "[62]\ttrain-error:0.250608 \n",
      "[63]\ttrain-error:0.249675 \n",
      "[64]\ttrain-error:0.249505 \n",
      "[65]\ttrain-error:0.248713 \n",
      "[66]\ttrain-error:0.248118 \n",
      "[67]\ttrain-error:0.247779 \n",
      "[68]\ttrain-error:0.246336 \n",
      "[69]\ttrain-error:0.245544 \n",
      "[70]\ttrain-error:0.244327 \n",
      "[71]\ttrain-error:0.243450 \n",
      "[72]\ttrain-error:0.241611 \n",
      "[73]\ttrain-error:0.240422 \n",
      "[74]\ttrain-error:0.240168 \n",
      "[75]\ttrain-error:0.239800 \n",
      "[76]\ttrain-error:0.238215 \n",
      "[77]\ttrain-error:0.237197 \n",
      "[78]\ttrain-error:0.236631 \n",
      "[79]\ttrain-error:0.235329 \n",
      "[80]\ttrain-error:0.234622 \n",
      "[81]\ttrain-error:0.234395 \n",
      "[82]\ttrain-error:0.232103 \n",
      "[83]\ttrain-error:0.230915 \n",
      "[84]\ttrain-error:0.230151 \n",
      "[85]\ttrain-error:0.230010 \n",
      "[86]\ttrain-error:0.228595 \n",
      "[87]\ttrain-error:0.228029 \n",
      "[88]\ttrain-error:0.226416 \n",
      "[89]\ttrain-error:0.225398 \n",
      "[90]\ttrain-error:0.223983 \n",
      "[91]\ttrain-error:0.223247 \n",
      "[92]\ttrain-error:0.222596 \n",
      "[93]\ttrain-error:0.221889 \n",
      "[94]\ttrain-error:0.220361 \n",
      "[95]\ttrain-error:0.219229 \n",
      "[96]\ttrain-error:0.218748 \n",
      "[97]\ttrain-error:0.217588 \n",
      "[98]\ttrain-error:0.216400 \n",
      "[99]\ttrain-error:0.216258 \n",
      "[100]\ttrain-error:0.215806 \n",
      "[101]\ttrain-error:0.214844 \n",
      "[102]\ttrain-error:0.214532 \n",
      "[103]\ttrain-error:0.214164 \n",
      "[104]\ttrain-error:0.213966 \n",
      "[105]\ttrain-error:0.213825 \n",
      "[106]\ttrain-error:0.212495 \n",
      "[107]\ttrain-error:0.211618 \n",
      "[108]\ttrain-error:0.211363 \n",
      "[109]\ttrain-error:0.211024 \n",
      "[110]\ttrain-error:0.210401 \n",
      "[111]\ttrain-error:0.209411 \n",
      "[112]\ttrain-error:0.208562 \n",
      "[113]\ttrain-error:0.207260 \n",
      "[114]\ttrain-error:0.205846 \n",
      "[115]\ttrain-error:0.204997 \n",
      "[116]\ttrain-error:0.204629 \n",
      "[117]\ttrain-error:0.203526 \n",
      "[118]\ttrain-error:0.202875 \n",
      "[119]\ttrain-error:0.202054 \n",
      "[120]\ttrain-error:0.201347 \n",
      "[121]\ttrain-error:0.201403 \n",
      "[122]\ttrain-error:0.201375 \n",
      "[123]\ttrain-error:0.201290 \n",
      "[124]\ttrain-error:0.200838 \n",
      "[125]\ttrain-error:0.199904 \n",
      "[126]\ttrain-error:0.199281 \n",
      "[127]\ttrain-error:0.198942 \n",
      "[128]\ttrain-error:0.197357 \n",
      "[129]\ttrain-error:0.196622 \n",
      "[130]\ttrain-error:0.196367 \n",
      "[131]\ttrain-error:0.196197 \n",
      "[132]\ttrain-error:0.195773 \n",
      "[133]\ttrain-error:0.194386 \n",
      "[134]\ttrain-error:0.193764 \n",
      "[135]\ttrain-error:0.193311 \n",
      "[136]\ttrain-error:0.192887 \n",
      "[137]\ttrain-error:0.192179 \n",
      "[138]\ttrain-error:0.191302 \n",
      "[139]\ttrain-error:0.191161 \n",
      "[140]\ttrain-error:0.190453 \n",
      "[141]\ttrain-error:0.190368 \n",
      "[142]\ttrain-error:0.190029 \n",
      "[143]\ttrain-error:0.189548 \n",
      "[144]\ttrain-error:0.188840 \n",
      "[145]\ttrain-error:0.188614 \n",
      "[146]\ttrain-error:0.188699 \n",
      "[147]\ttrain-error:0.188473 \n",
      "[148]\ttrain-error:0.188048 \n",
      "[149]\ttrain-error:0.187539 \n",
      "[150]\ttrain-error:0.187171 \n",
      "[151]\ttrain-error:0.186577 \n",
      "[152]\ttrain-error:0.184907 \n",
      "[153]\ttrain-error:0.183861 \n",
      "[154]\ttrain-error:0.183408 \n",
      "[155]\ttrain-error:0.183125 \n",
      "[156]\ttrain-error:0.182842 \n",
      "[157]\ttrain-error:0.181823 \n",
      "[158]\ttrain-error:0.181229 \n",
      "[159]\ttrain-error:0.181144 \n",
      "[160]\ttrain-error:0.180012 \n",
      "[161]\ttrain-error:0.179616 \n",
      "[162]\ttrain-error:0.179079 \n",
      "[163]\ttrain-error:0.178371 \n",
      "[164]\ttrain-error:0.177919 \n",
      "[165]\ttrain-error:0.177749 \n",
      "[166]\ttrain-error:0.177381 \n",
      "[167]\ttrain-error:0.177268 \n",
      "[168]\ttrain-error:0.176391 \n",
      "[169]\ttrain-error:0.175457 \n",
      "[170]\ttrain-error:0.174863 \n",
      "[171]\ttrain-error:0.173505 \n",
      "[172]\ttrain-error:0.172910 \n",
      "[173]\ttrain-error:0.171977 \n",
      "[174]\ttrain-error:0.171524 \n",
      "[175]\ttrain-error:0.170194 \n",
      "[176]\ttrain-error:0.169939 \n",
      "[177]\ttrain-error:0.169289 \n",
      "[178]\ttrain-error:0.168553 \n",
      "[179]\ttrain-error:0.168185 \n",
      "[180]\ttrain-error:0.167931 \n",
      "[181]\ttrain-error:0.167449 \n",
      "[182]\ttrain-error:0.167053 \n",
      "[183]\ttrain-error:0.166006 \n",
      "[184]\ttrain-error:0.165724 \n",
      "[185]\ttrain-error:0.164790 \n",
      "[186]\ttrain-error:0.163856 \n",
      "[187]\ttrain-error:0.163064 \n",
      "[188]\ttrain-error:0.162470 \n",
      "[189]\ttrain-error:0.161932 \n",
      "[190]\ttrain-error:0.161649 \n",
      "[191]\ttrain-error:0.161168 \n",
      "[192]\ttrain-error:0.160149 \n",
      "[193]\ttrain-error:0.159555 \n",
      "[194]\ttrain-error:0.159301 \n",
      "[195]\ttrain-error:0.158621 \n",
      "[196]\ttrain-error:0.157773 \n",
      "[197]\ttrain-error:0.157659 \n",
      "[198]\ttrain-error:0.156952 \n",
      "[199]\ttrain-error:0.155452 \n",
      "[200]\ttrain-error:0.154490 \n",
      "[201]\ttrain-error:0.154292 \n",
      "[202]\ttrain-error:0.153981 \n",
      "[203]\ttrain-error:0.153755 \n",
      "[204]\ttrain-error:0.153557 \n",
      "[205]\ttrain-error:0.152680 \n",
      "[206]\ttrain-error:0.151604 \n",
      "[207]\ttrain-error:0.151689 \n",
      "[208]\ttrain-error:0.151321 \n",
      "[209]\ttrain-error:0.150671 \n",
      "[210]\ttrain-error:0.149963 \n",
      "[211]\ttrain-error:0.148605 \n",
      "[212]\ttrain-error:0.148605 \n",
      "[213]\ttrain-error:0.147615 \n",
      "[214]\ttrain-error:0.147275 \n",
      "[215]\ttrain-error:0.147049 \n",
      "[216]\ttrain-error:0.146483 \n",
      "[217]\ttrain-error:0.145832 \n",
      "[218]\ttrain-error:0.144955 \n",
      "[219]\ttrain-error:0.143738 \n",
      "[220]\ttrain-error:0.142635 \n",
      "[221]\ttrain-error:0.142239 \n",
      "[222]\ttrain-error:0.142069 \n",
      "[223]\ttrain-error:0.141135 \n",
      "[224]\ttrain-error:0.140739 \n",
      "[225]\ttrain-error:0.140598 \n",
      "[226]\ttrain-error:0.140343 \n",
      "[227]\ttrain-error:0.140343 \n",
      "[228]\ttrain-error:0.140003 \n",
      "[229]\ttrain-error:0.139919 \n",
      "[230]\ttrain-error:0.139805 \n",
      "[231]\ttrain-error:0.139296 \n",
      "[232]\ttrain-error:0.138702 \n",
      "[233]\ttrain-error:0.138221 \n",
      "[234]\ttrain-error:0.137825 \n",
      "[235]\ttrain-error:0.137542 \n",
      "[236]\ttrain-error:0.136948 \n",
      "[237]\ttrain-error:0.136014 \n",
      "[238]\ttrain-error:0.135533 \n",
      "[239]\ttrain-error:0.135052 \n",
      "[240]\ttrain-error:0.134514 \n",
      "[241]\ttrain-error:0.134231 \n",
      "[242]\ttrain-error:0.134118 \n",
      "[243]\ttrain-error:0.133184 \n",
      "[244]\ttrain-error:0.132732 \n",
      "[245]\ttrain-error:0.132562 \n",
      "[246]\ttrain-error:0.131911 \n",
      "[247]\ttrain-error:0.130949 \n",
      "[248]\ttrain-error:0.130666 \n",
      "[249]\ttrain-error:0.130298 \n",
      "[250]\ttrain-error:0.130242 \n"
     ]
    }
   ],
   "source": [
    "predictors <- data.matrix(loan_data[, -which(names(loan_data) %in% 'outcome')])\n",
    "label <- as.numeric(loan_data$outcome) - 1\n",
    "test_idx <- sample(nrow(loan_data), 10000)\n",
    "xgb_default <- xgboost(data=predictors[-test_idx, ],\n",
    "                      label=label[-test_idx],\n",
    "                      objective=\"binary:logistic\",\n",
    "                      nrounds=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:08:24.021182Z",
     "start_time": "2019-04-29T02:08:23.981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>iter</th><th scope=col>train_error</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>250     </td><td>0.130242</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " iter & train\\_error\\\\\n",
       "\\hline\n",
       "\t 250      & 0.130242\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| iter | train_error |\n",
       "|---|---|\n",
       "| 250      | 0.130242 |\n",
       "\n"
      ],
      "text/plain": [
       "  iter train_error\n",
       "1 250  0.130242   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 最后得到的训练误差\n",
    "xgb_default$evaluation_log[250, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:10:09.067306Z",
     "start_time": "2019-04-29T02:10:08.934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.3581"
      ],
      "text/latex": [
       "0.3581"
      ],
      "text/markdown": [
       "0.3581"
      ],
      "text/plain": [
       "[1] 0.3581"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试误差\n",
    "pred_default <- predict(xgb_default, predictors[test_idx, ])\n",
    "error_default <- abs(label[test_idx] - pred_default) > 0.5\n",
    "mean(error_default)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试误差显著大于训练误差，我们认为模型出现了过拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:18:09.190082Z",
     "start_time": "2019-04-29T02:17:57.934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-error:0.337955 \n",
      "[2]\ttrain-error:0.340699 \n",
      "[3]\ttrain-error:0.341265 \n",
      "[4]\ttrain-error:0.343444 \n",
      "[5]\ttrain-error:0.340530 \n",
      "[6]\ttrain-error:0.339935 \n",
      "[7]\ttrain-error:0.339737 \n",
      "[8]\ttrain-error:0.337927 \n",
      "[9]\ttrain-error:0.337530 \n",
      "[10]\ttrain-error:0.336653 \n",
      "[11]\ttrain-error:0.335776 \n",
      "[12]\ttrain-error:0.335804 \n",
      "[13]\ttrain-error:0.335691 \n",
      "[14]\ttrain-error:0.335352 \n",
      "[15]\ttrain-error:0.334927 \n",
      "[16]\ttrain-error:0.334701 \n",
      "[17]\ttrain-error:0.333880 \n",
      "[18]\ttrain-error:0.332664 \n",
      "[19]\ttrain-error:0.332664 \n",
      "[20]\ttrain-error:0.332409 \n",
      "[21]\ttrain-error:0.331617 \n",
      "[22]\ttrain-error:0.331362 \n",
      "[23]\ttrain-error:0.331221 \n",
      "[24]\ttrain-error:0.330372 \n",
      "[25]\ttrain-error:0.330372 \n",
      "[26]\ttrain-error:0.330202 \n",
      "[27]\ttrain-error:0.330457 \n",
      "[28]\ttrain-error:0.330344 \n",
      "[29]\ttrain-error:0.330344 \n",
      "[30]\ttrain-error:0.330598 \n",
      "[31]\ttrain-error:0.330315 \n",
      "[32]\ttrain-error:0.330145 \n",
      "[33]\ttrain-error:0.329891 \n",
      "[34]\ttrain-error:0.329749 \n",
      "[35]\ttrain-error:0.329466 \n",
      "[36]\ttrain-error:0.329212 \n",
      "[37]\ttrain-error:0.329240 \n",
      "[38]\ttrain-error:0.329014 \n",
      "[39]\ttrain-error:0.329183 \n",
      "[40]\ttrain-error:0.329070 \n",
      "[41]\ttrain-error:0.328646 \n",
      "[42]\ttrain-error:0.328674 \n",
      "[43]\ttrain-error:0.328504 \n",
      "[44]\ttrain-error:0.328335 \n",
      "[45]\ttrain-error:0.327797 \n",
      "[46]\ttrain-error:0.326976 \n",
      "[47]\ttrain-error:0.326835 \n",
      "[48]\ttrain-error:0.327174 \n",
      "[49]\ttrain-error:0.327288 \n",
      "[50]\ttrain-error:0.327457 \n",
      "[51]\ttrain-error:0.327174 \n",
      "[52]\ttrain-error:0.327203 \n",
      "[53]\ttrain-error:0.327457 \n",
      "[54]\ttrain-error:0.326580 \n",
      "[55]\ttrain-error:0.326863 \n",
      "[56]\ttrain-error:0.326637 \n",
      "[57]\ttrain-error:0.326071 \n",
      "[58]\ttrain-error:0.326495 \n",
      "[59]\ttrain-error:0.326609 \n",
      "[60]\ttrain-error:0.326269 \n",
      "[61]\ttrain-error:0.326269 \n",
      "[62]\ttrain-error:0.326184 \n",
      "[63]\ttrain-error:0.325958 \n",
      "[64]\ttrain-error:0.325590 \n",
      "[65]\ttrain-error:0.325533 \n",
      "[66]\ttrain-error:0.325533 \n",
      "[67]\ttrain-error:0.325477 \n",
      "[68]\ttrain-error:0.325024 \n",
      "[69]\ttrain-error:0.324741 \n",
      "[70]\ttrain-error:0.325024 \n",
      "[71]\ttrain-error:0.324967 \n",
      "[72]\ttrain-error:0.324854 \n",
      "[73]\ttrain-error:0.324798 \n",
      "[74]\ttrain-error:0.324798 \n",
      "[75]\ttrain-error:0.324486 \n",
      "[76]\ttrain-error:0.324119 \n",
      "[77]\ttrain-error:0.323949 \n",
      "[78]\ttrain-error:0.324005 \n",
      "[79]\ttrain-error:0.323383 \n",
      "[80]\ttrain-error:0.323440 \n",
      "[81]\ttrain-error:0.323609 \n",
      "[82]\ttrain-error:0.323722 \n",
      "[83]\ttrain-error:0.323383 \n",
      "[84]\ttrain-error:0.323043 \n",
      "[85]\ttrain-error:0.322760 \n",
      "[86]\ttrain-error:0.322393 \n",
      "[87]\ttrain-error:0.322478 \n",
      "[88]\ttrain-error:0.322534 \n",
      "[89]\ttrain-error:0.322081 \n",
      "[90]\ttrain-error:0.322449 \n",
      "[91]\ttrain-error:0.322449 \n",
      "[92]\ttrain-error:0.321996 \n",
      "[93]\ttrain-error:0.321912 \n",
      "[94]\ttrain-error:0.322308 \n",
      "[95]\ttrain-error:0.322336 \n",
      "[96]\ttrain-error:0.322336 \n",
      "[97]\ttrain-error:0.322364 \n",
      "[98]\ttrain-error:0.322053 \n",
      "[99]\ttrain-error:0.321912 \n",
      "[100]\ttrain-error:0.322308 \n",
      "[101]\ttrain-error:0.322053 \n",
      "[102]\ttrain-error:0.322110 \n",
      "[103]\ttrain-error:0.321855 \n",
      "[104]\ttrain-error:0.321515 \n",
      "[105]\ttrain-error:0.321742 \n",
      "[106]\ttrain-error:0.321600 \n",
      "[107]\ttrain-error:0.321572 \n",
      "[108]\ttrain-error:0.321346 \n",
      "[109]\ttrain-error:0.321317 \n",
      "[110]\ttrain-error:0.320978 \n",
      "[111]\ttrain-error:0.320610 \n",
      "[112]\ttrain-error:0.320752 \n",
      "[113]\ttrain-error:0.320723 \n",
      "[114]\ttrain-error:0.320638 \n",
      "[115]\ttrain-error:0.320610 \n",
      "[116]\ttrain-error:0.320525 \n",
      "[117]\ttrain-error:0.320355 \n",
      "[118]\ttrain-error:0.320016 \n",
      "[119]\ttrain-error:0.320072 \n",
      "[120]\ttrain-error:0.320016 \n",
      "[121]\ttrain-error:0.319988 \n",
      "[122]\ttrain-error:0.320072 \n",
      "[123]\ttrain-error:0.319761 \n",
      "[124]\ttrain-error:0.320157 \n",
      "[125]\ttrain-error:0.320044 \n",
      "[126]\ttrain-error:0.320299 \n",
      "[127]\ttrain-error:0.319846 \n",
      "[128]\ttrain-error:0.319874 \n",
      "[129]\ttrain-error:0.319705 \n",
      "[130]\ttrain-error:0.319591 \n",
      "[131]\ttrain-error:0.319422 \n",
      "[132]\ttrain-error:0.319393 \n",
      "[133]\ttrain-error:0.319026 \n",
      "[134]\ttrain-error:0.318743 \n",
      "[135]\ttrain-error:0.318686 \n",
      "[136]\ttrain-error:0.318573 \n",
      "[137]\ttrain-error:0.318686 \n",
      "[138]\ttrain-error:0.318601 \n",
      "[139]\ttrain-error:0.318488 \n",
      "[140]\ttrain-error:0.318545 \n",
      "[141]\ttrain-error:0.318035 \n",
      "[142]\ttrain-error:0.317979 \n",
      "[143]\ttrain-error:0.317837 \n",
      "[144]\ttrain-error:0.317724 \n",
      "[145]\ttrain-error:0.317950 \n",
      "[146]\ttrain-error:0.317752 \n",
      "[147]\ttrain-error:0.317639 \n",
      "[148]\ttrain-error:0.317554 \n",
      "[149]\ttrain-error:0.317413 \n",
      "[150]\ttrain-error:0.317582 \n",
      "[151]\ttrain-error:0.317300 \n",
      "[152]\ttrain-error:0.317243 \n",
      "[153]\ttrain-error:0.317158 \n",
      "[154]\ttrain-error:0.316988 \n",
      "[155]\ttrain-error:0.316734 \n",
      "[156]\ttrain-error:0.316875 \n",
      "[157]\ttrain-error:0.316988 \n",
      "[158]\ttrain-error:0.316705 \n",
      "[159]\ttrain-error:0.316649 \n",
      "[160]\ttrain-error:0.316620 \n",
      "[161]\ttrain-error:0.316790 \n",
      "[162]\ttrain-error:0.316705 \n",
      "[163]\ttrain-error:0.316479 \n",
      "[164]\ttrain-error:0.316649 \n",
      "[165]\ttrain-error:0.316451 \n",
      "[166]\ttrain-error:0.316422 \n",
      "[167]\ttrain-error:0.316366 \n",
      "[168]\ttrain-error:0.316479 \n",
      "[169]\ttrain-error:0.316111 \n",
      "[170]\ttrain-error:0.315687 \n",
      "[171]\ttrain-error:0.315602 \n",
      "[172]\ttrain-error:0.315856 \n",
      "[173]\ttrain-error:0.315828 \n",
      "[174]\ttrain-error:0.315772 \n",
      "[175]\ttrain-error:0.315856 \n",
      "[176]\ttrain-error:0.315715 \n",
      "[177]\ttrain-error:0.315743 \n",
      "[178]\ttrain-error:0.315602 \n",
      "[179]\ttrain-error:0.315658 \n",
      "[180]\ttrain-error:0.315517 \n",
      "[181]\ttrain-error:0.315432 \n",
      "[182]\ttrain-error:0.315319 \n",
      "[183]\ttrain-error:0.315574 \n",
      "[184]\ttrain-error:0.315291 \n",
      "[185]\ttrain-error:0.315347 \n",
      "[186]\ttrain-error:0.315319 \n",
      "[187]\ttrain-error:0.315149 \n",
      "[188]\ttrain-error:0.315149 \n",
      "[189]\ttrain-error:0.315262 \n",
      "[190]\ttrain-error:0.314866 \n",
      "[191]\ttrain-error:0.314696 \n",
      "[192]\ttrain-error:0.314668 \n",
      "[193]\ttrain-error:0.314640 \n",
      "[194]\ttrain-error:0.314527 \n",
      "[195]\ttrain-error:0.314442 \n",
      "[196]\ttrain-error:0.314442 \n",
      "[197]\ttrain-error:0.314300 \n",
      "[198]\ttrain-error:0.314102 \n",
      "[199]\ttrain-error:0.314357 \n",
      "[200]\ttrain-error:0.314159 \n",
      "[201]\ttrain-error:0.314357 \n",
      "[202]\ttrain-error:0.314442 \n",
      "[203]\ttrain-error:0.314187 \n",
      "[204]\ttrain-error:0.314215 \n",
      "[205]\ttrain-error:0.313989 \n",
      "[206]\ttrain-error:0.314017 \n",
      "[207]\ttrain-error:0.313763 \n",
      "[208]\ttrain-error:0.314017 \n",
      "[209]\ttrain-error:0.313932 \n",
      "[210]\ttrain-error:0.313932 \n",
      "[211]\ttrain-error:0.313932 \n",
      "[212]\ttrain-error:0.313989 \n",
      "[213]\ttrain-error:0.313763 \n",
      "[214]\ttrain-error:0.313706 \n",
      "[215]\ttrain-error:0.313367 \n",
      "[216]\ttrain-error:0.313593 \n",
      "[217]\ttrain-error:0.313253 \n",
      "[218]\ttrain-error:0.313225 \n",
      "[219]\ttrain-error:0.313310 \n",
      "[220]\ttrain-error:0.313253 \n",
      "[221]\ttrain-error:0.312857 \n",
      "[222]\ttrain-error:0.313140 \n",
      "[223]\ttrain-error:0.312772 \n",
      "[224]\ttrain-error:0.312829 \n",
      "[225]\ttrain-error:0.312772 \n",
      "[226]\ttrain-error:0.312716 \n",
      "[227]\ttrain-error:0.312829 \n",
      "[228]\ttrain-error:0.312999 \n",
      "[229]\ttrain-error:0.312970 \n",
      "[230]\ttrain-error:0.312999 \n",
      "[231]\ttrain-error:0.312687 \n",
      "[232]\ttrain-error:0.312829 \n",
      "[233]\ttrain-error:0.312857 \n",
      "[234]\ttrain-error:0.312914 \n",
      "[235]\ttrain-error:0.312687 \n",
      "[236]\ttrain-error:0.312546 \n",
      "[237]\ttrain-error:0.312574 \n",
      "[238]\ttrain-error:0.312631 \n",
      "[239]\ttrain-error:0.312546 \n",
      "[240]\ttrain-error:0.312320 \n",
      "[241]\ttrain-error:0.312829 \n",
      "[242]\ttrain-error:0.312376 \n",
      "[243]\ttrain-error:0.312348 \n",
      "[244]\ttrain-error:0.312178 \n",
      "[245]\ttrain-error:0.312235 \n",
      "[246]\ttrain-error:0.312150 \n",
      "[247]\ttrain-error:0.312235 \n",
      "[248]\ttrain-error:0.312320 \n",
      "[249]\ttrain-error:0.311980 \n",
      "[250]\ttrain-error:0.312093 \n"
     ]
    }
   ],
   "source": [
    "xgb_penalty <- xgboost(data=predictors[-test_idx, ],\n",
    "                      label=label[-test_idx],\n",
    "                      params=list(eta=0.1, subsample=0.63, lambda=1000),\n",
    "                      objective=\"binary:logistic\",\n",
    "                      nrounds=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:18:29.450242Z",
     "start_time": "2019-04-29T02:18:29.418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>iter</th><th scope=col>train_error</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>250     </td><td>0.312093</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " iter & train\\_error\\\\\n",
       "\\hline\n",
       "\t 250      & 0.312093\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| iter | train_error |\n",
       "|---|---|\n",
       "| 250      | 0.312093 |\n",
       "\n"
      ],
      "text/plain": [
       "  iter train_error\n",
       "1 250  0.312093   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train-error\n",
    "xgb_penalty$evaluation_log[250, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:19:03.567971Z",
     "start_time": "2019-04-29T02:19:03.405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.3258"
      ],
      "text/latex": [
       "0.3258"
      ],
      "text/markdown": [
       "0.3258"
      ],
      "text/plain": [
       "[1] 0.3258"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test error\n",
    "pred_penalty <- predict(xgb_penalty, predictors[test_idx, ])\n",
    "mean(abs(pred_penalty - label[test_idx]) > 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此时训练误差和测试误差较为接近。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:28:56.474399Z",
     "start_time": "2019-04-29T02:28:34.414Z"
    }
   },
   "outputs": [],
   "source": [
    "error_default <- rep(0, 250)\n",
    "error_penalty <- rep(0, 250)\n",
    "for(i in 1:250){\n",
    "    pred_def <- predict(xgb_default, predictors[test_idx, ], ntreelimit = i)\n",
    "    error_default[i] <- mean(abs(pred_def - label[test_idx]) > 0.5)\n",
    "    pred_pen <- predict(xgb_penalty, predictors[test_idx, ], ntreelimit = i)\n",
    "    error_penalty[i] <- mean(abs(pred_pen - label[test_idx]) > 0.5)\n",
    "}\n",
    "errors <- rbind(xgb_default$evaluation_log,\n",
    "                xgb_penalty$evaluation_log,\n",
    "                data.frame(iter=1:250, train_error=error_default),\n",
    "                data.frame(iter=1:250, train_error=error_penalty))\n",
    "errors$type <- rep(c('default train', 'penalty train', \n",
    "                     'default test', 'penalty test'), rep(250, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:35:52.678439Z",
     "start_time": "2019-04-29T02:35:52.639Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>iter</th><th scope=col>train_error</th><th scope=col>type</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>229          </td><td>0.312970     </td><td>penalty train</td></tr>\n",
       "\t<tr><td>107          </td><td>0.342000     </td><td>default test </td></tr>\n",
       "\t<tr><td>111          </td><td>0.326900     </td><td>penalty test </td></tr>\n",
       "\t<tr><td> 99          </td><td>0.321912     </td><td>penalty train</td></tr>\n",
       "\t<tr><td> 79          </td><td>0.323383     </td><td>penalty train</td></tr>\n",
       "\t<tr><td>206          </td><td>0.323300     </td><td>penalty test </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " iter & train\\_error & type\\\\\n",
       "\\hline\n",
       "\t 229           & 0.312970      & penalty train\\\\\n",
       "\t 107           & 0.342000      & default test \\\\\n",
       "\t 111           & 0.326900      & penalty test \\\\\n",
       "\t  99           & 0.321912      & penalty train\\\\\n",
       "\t  79           & 0.323383      & penalty train\\\\\n",
       "\t 206           & 0.323300      & penalty test \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| iter | train_error | type |\n",
       "|---|---|---|\n",
       "| 229           | 0.312970      | penalty train |\n",
       "| 107           | 0.342000      | default test  |\n",
       "| 111           | 0.326900      | penalty test  |\n",
       "|  99           | 0.321912      | penalty train |\n",
       "|  79           | 0.323383      | penalty train |\n",
       "| 206           | 0.323300      | penalty test  |\n",
       "\n"
      ],
      "text/plain": [
       "  iter train_error type         \n",
       "1 229  0.312970    penalty train\n",
       "2 107  0.342000    default test \n",
       "3 111  0.326900    penalty test \n",
       "4  99  0.321912    penalty train\n",
       "5  79  0.323383    penalty train\n",
       "6 206  0.323300    penalty test "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors[(sample(nrow(errors), 6)), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:29.211863Z",
     "start_time": "2019-04-29T02:36:28.756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAIAAADXJFGjAAAACXBIWXMAABJ0AAASdAHeZh94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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(errors, aes(x=iter, y=train_error, group=type)) + \n",
    "    geom_line(aes(linetype=type, color=type))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:45:54.093846Z",
     "start_time": "2019-04-29T02:45:54.071Z"
    }
   },
   "source": [
    "##### Hyperparameters and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:55:59.972381Z",
     "start_time": "2019-04-29T02:55:59.937Z"
    }
   },
   "outputs": [],
   "source": [
    "# set up the folds and parameter list\n",
    "N <- nrow(loan_data)\n",
    "fold_number <- sample(1:5, N, replace = TRUE)\n",
    "params <- data.frame(eta=rep(c(.1, .5, .9), 3), \n",
    "                     max_depth=rep(c(3, 6, 12), rep(3, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T03:06:10.516415Z",
     "start_time": "2019-04-29T03:02:46.721Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>0.3288258</td><td>0.3222331</td><td>0.3240192</td><td>0.3344569</td><td>0.3333333</td></tr>\n",
       "\t<tr><td>0.3287131</td><td>0.3297421</td><td>0.3341902</td><td>0.3347831</td><td>0.3386973</td></tr>\n",
       "\t<tr><td>0.3408835</td><td>0.3395364</td><td>0.3396669</td><td>0.3429379</td><td>0.3488779</td></tr>\n",
       "\t<tr><td>0.3306288</td><td>0.3286538</td><td>0.3293842</td><td>0.3365228</td><td>0.3358511</td></tr>\n",
       "\t<tr><td>0.3494478</td><td>0.3407335</td><td>0.3510674</td><td>0.3490269</td><td>0.3547893</td></tr>\n",
       "\t<tr><td>0.3743520</td><td>0.3712047</td><td>0.3823628</td><td>0.3840383</td><td>0.3748221</td></tr>\n",
       "\t<tr><td>0.3501240</td><td>0.3454130</td><td>0.3451436</td><td>0.3464173</td><td>0.3489874</td></tr>\n",
       "\t<tr><td>0.3732252</td><td>0.3705517</td><td>0.3715212</td><td>0.3654453</td><td>0.3790914</td></tr>\n",
       "\t<tr><td>0.3856209</td><td>0.3885080</td><td>0.3833687</td><td>0.3816462</td><td>0.3908046</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       "\t 0.3288258 & 0.3222331 & 0.3240192 & 0.3344569 & 0.3333333\\\\\n",
       "\t 0.3287131 & 0.3297421 & 0.3341902 & 0.3347831 & 0.3386973\\\\\n",
       "\t 0.3408835 & 0.3395364 & 0.3396669 & 0.3429379 & 0.3488779\\\\\n",
       "\t 0.3306288 & 0.3286538 & 0.3293842 & 0.3365228 & 0.3358511\\\\\n",
       "\t 0.3494478 & 0.3407335 & 0.3510674 & 0.3490269 & 0.3547893\\\\\n",
       "\t 0.3743520 & 0.3712047 & 0.3823628 & 0.3840383 & 0.3748221\\\\\n",
       "\t 0.3501240 & 0.3454130 & 0.3451436 & 0.3464173 & 0.3489874\\\\\n",
       "\t 0.3732252 & 0.3705517 & 0.3715212 & 0.3654453 & 0.3790914\\\\\n",
       "\t 0.3856209 & 0.3885080 & 0.3833687 & 0.3816462 & 0.3908046\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 0.3288258 | 0.3222331 | 0.3240192 | 0.3344569 | 0.3333333 |\n",
       "| 0.3287131 | 0.3297421 | 0.3341902 | 0.3347831 | 0.3386973 |\n",
       "| 0.3408835 | 0.3395364 | 0.3396669 | 0.3429379 | 0.3488779 |\n",
       "| 0.3306288 | 0.3286538 | 0.3293842 | 0.3365228 | 0.3358511 |\n",
       "| 0.3494478 | 0.3407335 | 0.3510674 | 0.3490269 | 0.3547893 |\n",
       "| 0.3743520 | 0.3712047 | 0.3823628 | 0.3840383 | 0.3748221 |\n",
       "| 0.3501240 | 0.3454130 | 0.3451436 | 0.3464173 | 0.3489874 |\n",
       "| 0.3732252 | 0.3705517 | 0.3715212 | 0.3654453 | 0.3790914 |\n",
       "| 0.3856209 | 0.3885080 | 0.3833687 | 0.3816462 | 0.3908046 |\n",
       "\n"
      ],
      "text/plain": [
       "      [,1]      [,2]      [,3]      [,4]      [,5]     \n",
       " [1,] 0.3288258 0.3222331 0.3240192 0.3344569 0.3333333\n",
       " [2,] 0.3287131 0.3297421 0.3341902 0.3347831 0.3386973\n",
       " [3,] 0.3408835 0.3395364 0.3396669 0.3429379 0.3488779\n",
       " [4,] 0.3306288 0.3286538 0.3293842 0.3365228 0.3358511\n",
       " [5,] 0.3494478 0.3407335 0.3510674 0.3490269 0.3547893\n",
       " [6,] 0.3743520 0.3712047 0.3823628 0.3840383 0.3748221\n",
       " [7,] 0.3501240 0.3454130 0.3451436 0.3464173 0.3489874\n",
       " [8,] 0.3732252 0.3705517 0.3715212 0.3654453 0.3790914\n",
       " [9,] 0.3856209 0.3885080 0.3833687 0.3816462 0.3908046"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error <- matrix(0 , nrow=9, ncol=5)\n",
    "for(i in 1:nrow(params)){\n",
    "    for(k in 1:5){\n",
    "        fold_idx <- (1:N)[fold_number == k]\n",
    "        xgb <- xgboost(data=predictors[-fold_idx, ],\n",
    "                      label=label[-fold_idx],\n",
    "                      params=list(eta=params[i, 'eta'],\n",
    "                                 max_depth=params[i, 'max_depth']),\n",
    "                      objective=\"binary:logistic\",\n",
    "                      nrounds=100,\n",
    "                      verbose = 0)\n",
    "        pred <- predict(xgb, predictors[fold_idx, ])\n",
    "        error[i, k] <- mean(abs(pred - label[fold_idx]) >= 0.5)\n",
    "    }\n",
    "}\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T03:06:53.920322Z",
     "start_time": "2019-04-29T03:06:53.883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>eta</th><th scope=col>max_depth</th><th scope=col>avg_error</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.1     </td><td> 3      </td><td>32.85737</td></tr>\n",
       "\t<tr><td>0.5     </td><td> 3      </td><td>33.32252</td></tr>\n",
       "\t<tr><td>0.9     </td><td> 3      </td><td>34.23805</td></tr>\n",
       "\t<tr><td>0.1     </td><td> 6      </td><td>33.22081</td></tr>\n",
       "\t<tr><td>0.5     </td><td> 6      </td><td>34.90130</td></tr>\n",
       "\t<tr><td>0.9     </td><td> 6      </td><td>37.73560</td></tr>\n",
       "\t<tr><td>0.1     </td><td>12      </td><td>34.72171</td></tr>\n",
       "\t<tr><td>0.5     </td><td>12      </td><td>37.19669</td></tr>\n",
       "\t<tr><td>0.9     </td><td>12      </td><td>38.59897</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " eta & max\\_depth & avg\\_error\\\\\n",
       "\\hline\n",
       "\t 0.1      &  3       & 32.85737\\\\\n",
       "\t 0.5      &  3       & 33.32252\\\\\n",
       "\t 0.9      &  3       & 34.23805\\\\\n",
       "\t 0.1      &  6       & 33.22081\\\\\n",
       "\t 0.5      &  6       & 34.90130\\\\\n",
       "\t 0.9      &  6       & 37.73560\\\\\n",
       "\t 0.1      & 12       & 34.72171\\\\\n",
       "\t 0.5      & 12       & 37.19669\\\\\n",
       "\t 0.9      & 12       & 38.59897\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| eta | max_depth | avg_error |\n",
       "|---|---|---|\n",
       "| 0.1      |  3       | 32.85737 |\n",
       "| 0.5      |  3       | 33.32252 |\n",
       "| 0.9      |  3       | 34.23805 |\n",
       "| 0.1      |  6       | 33.22081 |\n",
       "| 0.5      |  6       | 34.90130 |\n",
       "| 0.9      |  6       | 37.73560 |\n",
       "| 0.1      | 12       | 34.72171 |\n",
       "| 0.5      | 12       | 37.19669 |\n",
       "| 0.9      | 12       | 38.59897 |\n",
       "\n"
      ],
      "text/plain": [
       "  eta max_depth avg_error\n",
       "1 0.1  3        32.85737 \n",
       "2 0.5  3        33.32252 \n",
       "3 0.9  3        34.23805 \n",
       "4 0.1  6        33.22081 \n",
       "5 0.5  6        34.90130 \n",
       "6 0.9  6        37.73560 \n",
       "7 0.1 12        34.72171 \n",
       "8 0.5 12        37.19669 \n",
       "9 0.9 12        38.59897 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_error <- 100 * rowMeans(error)\n",
    "cbind(params, avg_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 3.5",
   "language": "R",
   "name": "ir35"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "273.2px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
